The ending is a really powerful point. Most people apparently agree on two things:
1. AI is a great boon for all tasks and specialties we don’t have the skills to do ourselves. Understandable, since (A) we’re ill equipped to see the flaws in its output because it isn’t our area of expertise, and (B) it often can unlock great gains because if we trust it, we then don’t have to pay and wait for humans to do that thing.
2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
> Confidence in AI output is inversely proportional to one's ability to verify it
I like this / generally agree. The only wrinkle is that - for some tasks - the verification _is_ "run the script, see if it worked, don't care how... just that it did" which is distinctly different from "not only did it do it correctly, it did so in the most direct and performant way possible".
For a _lot_ of what I use LLMs to build, the former is all I need.
I was saying something like this a few years ago when people were getting first excited about ChatGPT. The gap has narrowed, but not by as much as people think.
AI produces output that is very convincing to a non-expert, and (dangerously), it's so good at looking like an expert, they might believe that it is an expert. But the moment you ask someone to use it for something they're an expert in themselves, the holes appear wide, consistent & obvious.
My favourite moment of seeing this in action was watching AI-worrier TV host/comedian Bill Maher. He has spent years talking about the dangers of AI taking everyone's jobs, destroying civilisation, ruining the economy, starting wars, "it's just getting better and better all the time", and so on. But one night he let slip a tell. "It's no good at writing jokes. Not yet, anyway". There you go, Bill... connect those dots...
There is real utility in it being a tool to help experts apply their expertise, as in this story where it speeds up some tasks to help the translator do part of the work, enhance their expertise, allow them to be more productive.
It's a better screwdriver, a better hammer, in the hands of somebody who knows what needs a screwdriver or a hammer. It doesn't replace them. It can't replace them. It's a tool that enhances the human, not an alternative.
I don't understand why this is not widely understood yet, but I'm sure it will in due course.
And I don't expect this to change. Even if the latest model scores 100% on every benchmark, all that really tells us is that it's now more productive/efficient than it was before at helping experts do that work, not that it can replace everyone in that category of work.
It seems to be a general principle: If AI is better than you at something, you use it. If AI is worse than you, you don't.
Each time the frontier models get better, I see another wave of AI doubters suddenly become believers. People say things like, "AI couldn't code last year, but now I use it for everything!" Interesting. Now we know how that the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped.
Meanwhile, the rest of us keep using AI as simple tools, like the person in the article. I wonder how long it will take before computers can program better than me, and I flip too.
Reminded me of this post by EY. (You're making a different point about existing expertise, not LLM expertise, but I think it holds in general.)
Every month a new guy discovers LLMs; discovers a skill the current LLMs require to get good results; and writes about the future jobs that will always be available for smart people like HIM, that are SKILLED in using LLMs.
The next generation of AIs doesn't need his fancy prompt. The image model goes from needing to type in just the right set of weird words and cryptic sorcerous invocations, to most people being able to type in English what they want and get a pretty good result.
There are still tasks that require careful invocation. But they are a much smaller fraction of all the tasks people are trying to do, or you can get a bleh result without the elaborate invocation to get it really good. And to improve on the bleh result you need to be substantially more of an expert than back when the Guy was memorizing a rule about adding "trending on Artstation" to the image prompts, as would always require a human paid to do that.
Another generation of AIs comes out. The next generation of Clever Skills is obsolete. Image models just obey the instructions for compositing panels without mixing them up, and you don't need to be an expert to get them to do it right. Another human value-add is gone. A wider set of tasks require no human expert.
Now a new Guy notices LLMs have become useful in his field for the first time. He discovers they require SKILL to use CORRECTLY. He posts about how there will always be jobs for humans who are SKILLED in using LLMs like HIM.
But it is not an infinite cycle. It is not the same each time it repeats. Now the Guy is a highly paid programmer or a career mathematician in 2026, instead of a graphic artist in 2023.
In six months the models will no longer require his vaunted Skills.
And by then there will be another Guy.
But the process doesn't continue forever. The Guys are coming from fields that were harder and harder for AIs. The brief centaur eras are shorter and shorter.
Today it is writers who are laughing at how bad the LLMs are at their job, and who will perhaps soon be posting about how it takes Skill to get an LLM to do their job Correctly. But the models are coming faster, and the eras of kinds of human value-add in each field are shortening.
There is a point when you run out of Guys, either because the centaur eras are too short for people to develop SKILLs and post to Twitter about them; or because there are not lands left for AIs to conquer; or because ordinary people are not reassured by some Nobel laureate proclaiming there will always be jobs for Nobel laureates with the SKILLS to prompt robotized biology labs Correctly.
But we'll never run out of amateur economists who assert entirely without a brief contemporary example that there will always be jobs for humans skilled at operating AIs!
We'll run out of professional economists saying it when nobody is paid for that work anymore.
I guess we'll also run out of amateur economists when they're dead.
I have no doubt that the writer is better at translating than AI, but I have to say that AI translation has gotten so good that I'm not sure how much longer translation work will be there, or rather it might end up being more about auditing.
For example, I just read the Lawrence Ellsworth translation of The Three Musketeers, which I very thoroughly enjoyed. I don't speak or read French, but from my understanding Ellsworth's translation is considered one of the more accurate translations of the work.
Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
They were honestly remarkably similar. As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation. I do think that the prose for the Ellsworth translation was a bit better, but the prose for the Fable one was actually perfectly readable. Again, I don't speak French so I cannot say for sure, but I do not believe that I would have gotten a significantly different experience had I read the Fable version instead of the Ellsworth version.
Now, it's possible (and likely) that this is somewhat self-fulfilling; Fable might have been trained using Ellsworth's translation and as such it's very directly able to crib from it; sadly since I do not speak any language outside of English, there's sort of a catch-22: the only way I can compare the accuracy of a translation is to compare against other translations, but if other translations exist then that will likely influence the results, and if a translation doesn't already exist then I have no way of auditing it.
I'm still going to continue reading through Ellsworth's translations for the subsequent stories simply because that feels more canonical, and as I said I do think the prose was a bit better.
> Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
This isn’t a great test, because Claude almost certainly has multiple translations of The Three Musketeers in its training data.
Oops, I legitimately missed the second-to-last paragraph.
I still think there are better tests you could do. Ideally, you would choose a book that was published recently—after the model’s cut-off date—which is considered to be a good translation. But even something like The Girl With the Dragon Tattoo, which is not particularly new and by no means obscure, would be better than a famous work of literature like The Three Musketeers that has many translations.
Almost certainly correct, though I've noticed that these LLMs like to complain when you give it stuff that is still in copyright. The Three Musketeers is thoroughly public domain everywhere so in that sense it's a good test, but of course because it's public domain everywhere there are lots of translations to crib from so I acknowledge it's not a great test because the training data almost certainly contains a competent translation.
Even if Fable didn't have Ellsworth's translation, it certainly has the William Barrow translation, which would still get it like 80+% of the way there.
My wife speaks Spanish, I should get her to do some kind of comparison with a Spanish book that doesn't have English translations.
> As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation.
Crucially the full translation was part of ChatGPT’s training set. Recall is a pretty solved problem in machine learning.
How well does it translate a French novel published yesterday? Where neither the original novel nor any translations are in the training set yet? Or might not even exist!
I tried asking ChatGPT to translate a letter I wrote in Slovenian this weekend. It got the general gist but missed a lot of the nuance. Completely missed several of the little touches of tone where the right choice of synonym conveys a whole bunch of information.
Guess I have no way of proving it, but I pinky swear that I didn't edit it in later!
But yeah, I broadly do agree; if I read other languages I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language (and sometimes my comprehension of even that is questionable).
You're very likely to get a somewhat circular reference; the key (for me) is that for 90% of the usages, "standard translation LLMs" are just fine - I still recommend a translator but they're more of a proof-reader for both languages, catching where something slipped through.
I see the difficulties more in other areas, such as technical translations, specialist books, user manuals, and translating UIs, where contextual information and a back and forth with the client is needed to clarify details, and (for user manuals and UIs) the translator has to put themselves in the mind of the user and has to consider the possible contexts and use cases.
Yeah, that's why I put the caveat in there. I have no real way to verify the result outside of checking against "known good" translations, though if the known-good translation exists then there's not exactly a lot of reason to do the AI translation in the first place.
I suspect if I knew another language I would be able to find errors in the translation.
Yes, it is another variation on the Gell-Mann Amnesia Effect. I have a number of non-developers in my circle of friends who think Claude is about to put me out of work. They think it is just a great tool for them, not a replacement. Of course!
This is sort of missing the point-- people who dont deal with linguistics dont understand that there are multiple types of translation. There's word for word (which is what you're talking about) and sense for sense. If you let an LLM do all of your translation you're letting it interpret huge amounts of intent and context it doesnt (and probably cant) access. The ways in which this impacts the translation will forever be unknown to you and in the worst case lost forever.
So i guess in the end it just matters how important the work is.
A raw "word for word" translation (which I also tried) made the story somewhat hard to follow and very dry, but just asking it to keep the same kind of jovial swashbuckling tone of the original made something pretty similar to Ellsworth's translation.
Again, before someone decides to "correct" me on this, I am aware that it's very likely that the Ellsworth translations are part of the training set so it's not directly a fair comparison.
LLMs are now being aggressively manipulated for propaganda purposes. Powerful people have realized that people believe LLMs, and treat them as authoritative sources of fact.
The number of lies, lies by omission, deceptive distortions, and fallacious argument tactics they generate is absurd, and increasing rapidly. Translation, when done as a service you are paid for, can't be relied on by propaganda bots.
More specifically, it is coming for coders. If you make your living by banging out lines of code all day, then you may want to be looking at adjusting your career trajectory. But if that is your job, you are either very junior, or a bit foolish for getting into that situation.
I think this collapses a global, complex heirarchy of software engineering workers into a single monolith and serves only to advertise for frontier LLM providers. the point where you no longer need engineers is not going to be reached by making LLMs better and better.
I think there is going to be a long time before all of the obscure knowledge of a decent software developer can be completely replaced by AI. Though the job is going to change beyond recognition. It already has in many ways.
An honest to god article full of em dashes that's not because it was AI but because it was a human using them as a crutch to get around crafting sentences that flow naturally. Almost brings a tear to my eye.
I wish more people had casual exposure to professional translators. Its a deeply important, vanishingly small segment of the human population and has been this way for at least many thousands of years. Also, it will continue to be!
My writing used to be littered with them, but I now eschew the em in favour of en, as it has become too strong an anti-shibboleth.
I have also taken to being sloppier in my prose, as I’ve had stories rejected for being “written by AI” - when they’re shorts I wrote more than a decade ago. Reworked them to sound like a moron, accepted. Sigh.
I have a similar issue. I tend to have a very “structured” type of writing. Say on slack or Reddit for example. Using markdown formatting. Lists with bulletpoints etc. And I tend to write long detailed explanations, sometimes too long if I am being honest.
But now I find myself adding noise and imperfections to my writing (not that it was perfect) to make it more human, which is kinda silly.
I think it's an interesting perspective, because translation is one of the jobs that I (a) hear is the first to lose work due to AI, and (b) often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
> often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
As one of such people, I think there is a nuance to it. AI is great when you’re translating something to yourself. But when translating things for others, more caution and human judgement is needed. Espesially when translating instruction manuals, where bad wording could cause someone to injure themself.
This. I put things through Google translate all the time and they're always unreliable. Sometimes they're correct, sometimes I need to know roughly what the original said. Infamously, Google used to say "geiler Typ" meant "horny guy" when it means "awesome guy". Google used to think "geil" meant "horny" in general, which it can but not usually
Google Translate is at the bottom of the barrel. All other AI translation tools are vastly superior. You'd want to evaluate those, and forget about Google Translate completely.
Language is incredibly complex. I remember a TikTok from a bilingual English-Korean speaker comparing the English subtitles from a Squid Game scene to what was actually being said by the characters. The nuance and info density lost in translation made the subtitles feel completely remedial. Americans were basically watching a different show altogether.
There are translators and there are translators. Translating legal/business documents is a completely different thing from translating movies/books/games.
I can confidently say that LLMs do a better job than the average traditionally published fictions in my country, at least when the original works are in English. Every single time I watch a subbed movie there will be some lines noticeably wrong.
Translators already started losing jobs due to machine translation a decade ago (e.g. DeepL), before LLMs. Remuneration going down made it more difficult to make a living as a translator already then, even if you still received offers.
Not all translations are the same. Literary translations are often works of art in and of themselves, and automating them would be missing the point entirely, like automating homework or weightlifting at the gym. I don't really know what's the state of the art, but I do buy that, on the other hand, translating toaster manuals or generic copy could soon be automatic.
Yup. If you are bilingual, you quickly realize how some translations are very bad. How some translations are very good. And how hard it is to translate. With dry, simple text, it might be easy. But when it involves art?
Some jokes don't translate directly. There is pun. Sounds of words. Double meaning. Ambiguity. Cultural background. The creation of new words.
It can be reasonably argued that some poetry can be impossible to translate from some languages to others. A poem might be explained, but by a lenghty, dissecting explanation, that completely loses the point of it.
On the other hand, a lot of people become extremely put off by the smallest sign of ai slop. And the llms have a tendency to impart their style to any text they touch.
It'll be a similar theme for all facets of work involving any language, slowly - human or code. We'll parrot about humans in the loop this and that, but I think it'll be less humans in the loop over time and I think most people will even be willing to settle for a slightly more mediocre translation or coded project. It all comes back to our dopamine addiction, where we like fast feedback. And the oligarchs like tools to suppress wages. We will be our own demise for not advocating for either UBI or job protections, instead, happily using the technology while also rolling our eyes that it could never replace us.
Is it that unfortunate? Tasks that don't require high-quality translation now don't need human labor. We should be celebrating.
The sad part is that we haven't figured out how to distribute our resources fairly to these people even thought their services aren't required as often. Instead we just take their wages and give them to the top 0.1%
It’s unfortunate because we are seeing more poor translations in all domains, and users suffer from it. It’s part of a general enshittification of things. There are few contexts where low-quality translations don’t constitute a degradation of user experience.
Just one funny example I saw recently: On the Amazon website, a submit button labeled “Go” in English was translated to something that translated back would be “Walking”. That’s the kind of thing that would be exceedingly unlikely to happen with a human translator.
AI isn’t replacing me. Like a toddler, it
needs to be constantly coached.
Like a toddler, it will grow up.
Humans are really bad at noticing trajectories. They see the current situation. They know what the situation was 5 years ago. But for some reason they do not believe that there is a trajectory. They view the present state as the final destination.
I worked at large Japanese bank in New York and happened to sit near Chief US Economist next to his Japanese translator. She would occasionally ask about certain idioms. I remember explaining what a wildcat strike was for instance. But it must have been pretty tough because the guy was prolific in his commentary.
Presumably the people paying the author for translation services are aware of AI, but for whatever reason are choosing a humans services instead. IMO it would be a form fraud to heavily rely on AI and not disclose that to the customer.
All I got out of this article is that he should have went home and dumped it into chatgpt just to see what happened; then if it did as good a job as him, he should start looking for other places he can add value that AI can't.
The article does not say that. The author doesn't take the text the other person dumped into ChatGPT and evaluate its quality. That is what OP is referring to.
wrt. the end of the story, it will be interesting to see if people start noticing their Dunning-Kruger bias as a result of LLMs.
Specifically: LLMs make it really easy to misunderestimate the complexity of fields other than your own. (You can see this with a lot of vibecoded projects, for example – once they hit the wall of complexity, they stall out or start finding ugly patches for fundamental design issues, etc.)
I don't think this sort of cultural change will happen short-term, though.
> LLMs make it really easy to misunderestimate the complexity
In my experience this is a real problem. Just yesterday I asked my LLM to create a piece of software that could help me build an 'ambilight-like experience' through my home assistant. It did something that seems to work as I expected, but there is a lot of theory that I just brushed past. It would be pretty easy for me to assume that I would be able to replicate this feature from scratch 'now that I understand the problem'.
Agreed. LLMs are really terrific at sounding like they know exactly what they are talking about. Fable is the best yet. Beautiful, thorough explanations with absolute certainty, which under even light scrutiny turn out to be mostly bullshit.
I still love the tool, but remain as convinced as ever that AGI does not lie at the end of this particular path.
Translating is one thing that artificial intelligence undeniably excels at, and the value of this alone is enough to underpin the trillion dollar valuations of the gigantic AI companies.
Translation is a gigantic boon for business, but just as important for human connection, for culture, science, art, and entertainment. The value of automatic and cheap translation between all languages, this tower of Babylon, is immeasurable.
Human translators will always be better than any AI at their job. But they don't have unlimited time and energy, and they aren't cheap. AI makes good to great translations available to everybody.
I agree with the take, but it's a temporary one, the sad reality is that we will be literally inferior soon, there will be a point where we will not trust human input without counter check by AI, we need to remember that we are kinda at the beginning of the AI era, in 5 to 10 years it's very unlikely that a human translator or software engineers will do better than the tooling we will have.
There is already a tipping point now in software engineering where we prefer to ask AI instead of humans because we believe accuracy will be better, see SO death as an example or just see the current state of online dev communities, it's getting deserted and between team members at work, we can also notice that people speak less and less.
This plague of misanthropic doom is itself pretty depressing. Why do so many people think LLMs are in any way on a path to compete with human brains? Why do you think so little of yourself? The brain is magnificent and complex in ways that we are unable to decipher anytime soon, and it does way more than an LLM. Way, way more.
I don't talk specifically about LLMs but AI in general, it's an important distinction because tooling is currently what make models useful and more performant.
When I say we, I mean the general population really. There0-'ll always be the super bright ones, sure, but we gotta be realistic here. Most people already struggle to make any meaningful contribution because it's so hard to compete, and that gap is just gonna get bigger and bigger.
I agree the brain is pretty magnificent, but when it comes to stuff like language, figuring out if an idea actually works, or running business stuff, it's pretty obvious we'll be inferior. AI can already innovate and come up with new things way faster than any human could, so at some point (soon) => the majority of contributions are just gonna come from AI, not from us.
The thing is that AI is not some inevitable force of nature that must just be contended with and weathered. It is an active choice by our society to develop it and it is a choice by our society how we should use it, if at all.
We would all do well to remember that and remember that each and every advancement and use case regarding AI is the result of choices by people (or the groups of people we call corporations) and are oftentimes motivated by the profit motive, not the best interest of humanity.
We could make different choices up to and including our own Butlerian Jihad where we ban all forms of AI but we could also do everything we can to prevent the worst fallout short of that.
There are only two types of problems in the universe:
1) those posed by the laws of physics
2) those posed by human choices
This is anecdata, but in my experience with myself and my coworkers, it is not that we believe the AI will be more accurate in software engineering, but that the answer will come faster and be more tailored to our exact problems. If I have to search SO, I have to find the answer and then tweak it to fit my codebase, but with AI tooling, the AI is already basing its answer around my code.
I think we actually do believe it, do you believe Fable 5+GPT-5.5(+ the whole model zoo) in loop with adversarial (no budget limit) or a 10-year experienced SWE?
We are talking about "codebases" but realistically we won't even be checking the filetree of them soon, it will be all blind, containerized and verified with pseudo guarantees which are good enough to build serious things. We don't even write documentation for humans anymore, we need to look at the trends and the reality within companies, most developers became "callcenter agents" in a matter of only 2 years and literally most of them are not even using proper automated tooling yet as we can see the "vibe coding" trend with Claude Code which is weak, by far most work done daily by developers is already automatable entirely, but with exceptions, sure, but in a few years those exceptions will become rare.
There will be niche problems about legacy products, sure, but legacy products will all be replaced over time, if we think in depth, why do we even need that many languages, that many tools? Tomorrow AI will write 99% if not all code existing ("code" doesn't even matter anyway), so it's much better if it's specific to AI and not playing this dance where we think we are doing a meaningful human contribution on an "AI-made codebase".
For context, I have 2 decades of software dev behind me.
> there will be a point where we will not trust human input without counter check by AI
That's nonsense. There is zero reason to believe that AI (with the current techniques) will ever become reliable enough to let it do its own thing, let alone better than a human. It's been years of development and you still can't trust it to get basic facts correct, not even "well it's better than it used to be". Saying it'll replace humans in 5-10 years is a fantasy (or a prediction that people are stupid enough to fall for hype, I guess).
You come from the principle that humans are reliable at first which is partly right but also wrong in so many scenarios, you can even see lately the CVE spree happening, which demonstrates that human-made codebases have serious vulnerabilities and without the help of AI, we probably won't even know about them which proves that humans are not that "reliable", the current societal structure is also built around the fact that humans can't really be trusted, nothing really different with AI, we can't fully trust them like we can't fully trust humans.
It's not a fantasy, I would bet that no serious engineer nowadays is putting in prod a codebase not AI reviewed meaning we already can't work on our own, we must factor in the on-going decline of human capabilities (at least developers) as well of course.
I'm not really saying this because of any sort of hype, but I can personally relate where I went from actually coding to NEVER CODE in less than 2 years, and everyone around me is the same thing, what it will be in 5 years?
Knowing that really, most developers aren't even using proper tooling yet so they are very slow compared to what they could be, I mean how many people we hear saying they can't even saturate an Anthropic Max 20 subscription? I saturated 7 accounts the last 2h alone, it's because they haven't entirely rethought their workflows yet.
The ending is a really powerful point. Most people apparently agree on two things:
1. AI is a great boon for all tasks and specialties we don’t have the skills to do ourselves. Understandable, since (A) we’re ill equipped to see the flaws in its output because it isn’t our area of expertise, and (B) it often can unlock great gains because if we trust it, we then don’t have to pay and wait for humans to do that thing.
2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
Well said. Everyone agrees AI can't do their job, so it ends up doing everyone else's.
I'm not sure how to formulate it yet but it seems there is some Peter Principle/Gell-Mann Effect corollary that is AI-related we can say here.
Perhaps: "AI rises to the level of its users' incompetence."
Or: "Confidence in AI output is inversely proportional to one's ability to verify it"
> Confidence in AI output is inversely proportional to one's ability to verify it
I like this / generally agree. The only wrinkle is that - for some tasks - the verification _is_ "run the script, see if it worked, don't care how... just that it did" which is distinctly different from "not only did it do it correctly, it did so in the most direct and performant way possible".
For a _lot_ of what I use LLMs to build, the former is all I need.
I was saying something like this a few years ago when people were getting first excited about ChatGPT. The gap has narrowed, but not by as much as people think.
AI produces output that is very convincing to a non-expert, and (dangerously), it's so good at looking like an expert, they might believe that it is an expert. But the moment you ask someone to use it for something they're an expert in themselves, the holes appear wide, consistent & obvious.
My favourite moment of seeing this in action was watching AI-worrier TV host/comedian Bill Maher. He has spent years talking about the dangers of AI taking everyone's jobs, destroying civilisation, ruining the economy, starting wars, "it's just getting better and better all the time", and so on. But one night he let slip a tell. "It's no good at writing jokes. Not yet, anyway". There you go, Bill... connect those dots...
There is real utility in it being a tool to help experts apply their expertise, as in this story where it speeds up some tasks to help the translator do part of the work, enhance their expertise, allow them to be more productive.
It's a better screwdriver, a better hammer, in the hands of somebody who knows what needs a screwdriver or a hammer. It doesn't replace them. It can't replace them. It's a tool that enhances the human, not an alternative.
I don't understand why this is not widely understood yet, but I'm sure it will in due course.
And I don't expect this to change. Even if the latest model scores 100% on every benchmark, all that really tells us is that it's now more productive/efficient than it was before at helping experts do that work, not that it can replace everyone in that category of work.
It seems to be a general principle: If AI is better than you at something, you use it. If AI is worse than you, you don't.
Each time the frontier models get better, I see another wave of AI doubters suddenly become believers. People say things like, "AI couldn't code last year, but now I use it for everything!" Interesting. Now we know how that the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped.
Meanwhile, the rest of us keep using AI as simple tools, like the person in the article. I wonder how long it will take before computers can program better than me, and I flip too.
Reminded me of this post by EY. (You're making a different point about existing expertise, not LLM expertise, but I think it holds in general.)
Every month a new guy discovers LLMs; discovers a skill the current LLMs require to get good results; and writes about the future jobs that will always be available for smart people like HIM, that are SKILLED in using LLMs.
The next generation of AIs doesn't need his fancy prompt. The image model goes from needing to type in just the right set of weird words and cryptic sorcerous invocations, to most people being able to type in English what they want and get a pretty good result.
There are still tasks that require careful invocation. But they are a much smaller fraction of all the tasks people are trying to do, or you can get a bleh result without the elaborate invocation to get it really good. And to improve on the bleh result you need to be substantially more of an expert than back when the Guy was memorizing a rule about adding "trending on Artstation" to the image prompts, as would always require a human paid to do that.
Another generation of AIs comes out. The next generation of Clever Skills is obsolete. Image models just obey the instructions for compositing panels without mixing them up, and you don't need to be an expert to get them to do it right. Another human value-add is gone. A wider set of tasks require no human expert.
Now a new Guy notices LLMs have become useful in his field for the first time. He discovers they require SKILL to use CORRECTLY. He posts about how there will always be jobs for humans who are SKILLED in using LLMs like HIM.
But it is not an infinite cycle. It is not the same each time it repeats. Now the Guy is a highly paid programmer or a career mathematician in 2026, instead of a graphic artist in 2023.
In six months the models will no longer require his vaunted Skills.
And by then there will be another Guy.
But the process doesn't continue forever. The Guys are coming from fields that were harder and harder for AIs. The brief centaur eras are shorter and shorter.
Today it is writers who are laughing at how bad the LLMs are at their job, and who will perhaps soon be posting about how it takes Skill to get an LLM to do their job Correctly. But the models are coming faster, and the eras of kinds of human value-add in each field are shortening.
There is a point when you run out of Guys, either because the centaur eras are too short for people to develop SKILLs and post to Twitter about them; or because there are not lands left for AIs to conquer; or because ordinary people are not reassured by some Nobel laureate proclaiming there will always be jobs for Nobel laureates with the SKILLS to prompt robotized biology labs Correctly.
But we'll never run out of amateur economists who assert entirely without a brief contemporary example that there will always be jobs for humans skilled at operating AIs!
We'll run out of professional economists saying it when nobody is paid for that work anymore.
I guess we'll also run out of amateur economists when they're dead.
Source: https://x.com/allTheYud/status/2057136382817231151
I have no doubt that the writer is better at translating than AI, but I have to say that AI translation has gotten so good that I'm not sure how much longer translation work will be there, or rather it might end up being more about auditing.
For example, I just read the Lawrence Ellsworth translation of The Three Musketeers, which I very thoroughly enjoyed. I don't speak or read French, but from my understanding Ellsworth's translation is considered one of the more accurate translations of the work.
Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
They were honestly remarkably similar. As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation. I do think that the prose for the Ellsworth translation was a bit better, but the prose for the Fable one was actually perfectly readable. Again, I don't speak French so I cannot say for sure, but I do not believe that I would have gotten a significantly different experience had I read the Fable version instead of the Ellsworth version.
Now, it's possible (and likely) that this is somewhat self-fulfilling; Fable might have been trained using Ellsworth's translation and as such it's very directly able to crib from it; sadly since I do not speak any language outside of English, there's sort of a catch-22: the only way I can compare the accuracy of a translation is to compare against other translations, but if other translations exist then that will likely influence the results, and if a translation doesn't already exist then I have no way of auditing it.
I'm still going to continue reading through Ellsworth's translations for the subsequent stories simply because that feels more canonical, and as I said I do think the prose was a bit better.
> Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
This isn’t a great test, because Claude almost certainly has multiple translations of The Three Musketeers in its training data.
Read the last two paragraphs :)
Oops, I legitimately missed the second-to-last paragraph.
I still think there are better tests you could do. Ideally, you would choose a book that was published recently—after the model’s cut-off date—which is considered to be a good translation. But even something like The Girl With the Dragon Tattoo, which is not particularly new and by no means obscure, would be better than a famous work of literature like The Three Musketeers that has many translations.
Almost certainly correct, though I've noticed that these LLMs like to complain when you give it stuff that is still in copyright. The Three Musketeers is thoroughly public domain everywhere so in that sense it's a good test, but of course because it's public domain everywhere there are lots of translations to crib from so I acknowledge it's not a great test because the training data almost certainly contains a competent translation.
Even if Fable didn't have Ellsworth's translation, it certainly has the William Barrow translation, which would still get it like 80+% of the way there.
My wife speaks Spanish, I should get her to do some kind of comparison with a Spanish book that doesn't have English translations.
They say "yes, I admit it, this is all invalid".
> I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
I'm pretty sure the Ellsworth translation is in the corpus. You basically instructed claude to regurgitate it.
The llms all have the more famous books memorized. You can trick them to recite them more or less word for word.
I mentioned this specifically in my comment :)
> As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation.
Crucially the full translation was part of ChatGPT’s training set. Recall is a pretty solved problem in machine learning.
How well does it translate a French novel published yesterday? Where neither the original novel nor any translations are in the training set yet? Or might not even exist!
I tried asking ChatGPT to translate a letter I wrote in Slovenian this weekend. It got the general gist but missed a lot of the nuance. Completely missed several of the little touches of tone where the right choice of synonym conveys a whole bunch of information.
Did no one actually finish reading my comment?
I feel like that wasn’t there when I started writing my comment. I also have a bad habit of quickly posting and then adding over a few minutes.
Glad we agree :)
Guess I have no way of proving it, but I pinky swear that I didn't edit it in later!
But yeah, I broadly do agree; if I read other languages I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language (and sometimes my comprehension of even that is questionable).
Welcome to the internet
You're very likely to get a somewhat circular reference; the key (for me) is that for 90% of the usages, "standard translation LLMs" are just fine - I still recommend a translator but they're more of a proof-reader for both languages, catching where something slipped through.
I see the difficulties more in other areas, such as technical translations, specialist books, user manuals, and translating UIs, where contextual information and a back and forth with the client is needed to clarify details, and (for user manuals and UIs) the translator has to put themselves in the mind of the user and has to consider the possible contexts and use cases.
> Again, I don't speak French so I cannot say for sure
This reminds me of the adage, that ChatGPT is really great at everything except my own work.
Yeah, that's why I put the caveat in there. I have no real way to verify the result outside of checking against "known good" translations, though if the known-good translation exists then there's not exactly a lot of reason to do the AI translation in the first place.
I suspect if I knew another language I would be able to find errors in the translation.
Yes, it is another variation on the Gell-Mann Amnesia Effect. I have a number of non-developers in my circle of friends who think Claude is about to put me out of work. They think it is just a great tool for them, not a replacement. Of course!
This is sort of missing the point-- people who dont deal with linguistics dont understand that there are multiple types of translation. There's word for word (which is what you're talking about) and sense for sense. If you let an LLM do all of your translation you're letting it interpret huge amounts of intent and context it doesnt (and probably cant) access. The ways in which this impacts the translation will forever be unknown to you and in the worst case lost forever.
So i guess in the end it just matters how important the work is.
Actually I was talking about tonally as well.
A raw "word for word" translation (which I also tried) made the story somewhat hard to follow and very dry, but just asking it to keep the same kind of jovial swashbuckling tone of the original made something pretty similar to Ellsworth's translation.
Again, before someone decides to "correct" me on this, I am aware that it's very likely that the Ellsworth translations are part of the training set so it's not directly a fair comparison.
LLMs are now being aggressively manipulated for propaganda purposes. Powerful people have realized that people believe LLMs, and treat them as authoritative sources of fact.
The number of lies, lies by omission, deceptive distortions, and fallacious argument tactics they generate is absurd, and increasing rapidly. Translation, when done as a service you are paid for, can't be relied on by propaganda bots.
An interesting counter-example: https://xcancel.com/ValerioCapraro/status/206506665753442336...
I wonder if “Just 3 words: you’re not alone” would have been acceptable. :)
There is a the possibility that the AI was trained on Lawrence's translation.
Already mentioned in the comment lol.
This moment is coming for software developers too
More specifically, it is coming for coders. If you make your living by banging out lines of code all day, then you may want to be looking at adjusting your career trajectory. But if that is your job, you are either very junior, or a bit foolish for getting into that situation.
so what is software developer doing if writing code is not part of their job
If you get to senior level then most of your job probably is not writing code, but planning things out. The code is largely an implementation detail.
At least that's how it was for me, maybe other peoples' careers are different.
Same thing architects do if drawing lines gets automated: architecture.
Would you trust living in a high rise designed by AI?
Designing a system that survives production is the job.
So what a lab researcher doing if typing articles is not part of the job?
Yeah almost certainly, especially the ones who made a career out of "copypaste from StackOverflow", which is most engineers.
But even the good engineers should likely be a little worried.
I think this collapses a global, complex heirarchy of software engineering workers into a single monolith and serves only to advertise for frontier LLM providers. the point where you no longer need engineers is not going to be reached by making LLMs better and better.
I think there is going to be a long time before all of the obscure knowledge of a decent software developer can be completely replaced by AI. Though the job is going to change beyond recognition. It already has in many ways.
But not before a huge crash in optimism about their capabilities. Specifically wrt accuracy, reliability, efficiency, and organization/architecture.
An honest to god article full of em dashes that's not because it was AI but because it was a human using them as a crutch to get around crafting sentences that flow naturally. Almost brings a tear to my eye.
I wish more people had casual exposure to professional translators. Its a deeply important, vanishingly small segment of the human population and has been this way for at least many thousands of years. Also, it will continue to be!
I’ve a friend who does simultaneous interpretation at the UN and she’s just… good god, how do you even do that. Oh, and she does it in six languages.
And here I am, brain the size of a galaxy, and I fumble my way through every language I speak other than English.
Serious respect for the linguists.
My first rule—before doing anything else—when writing a sentence, is to check whether I could have removed the em dashes by re-ordering the elements.
Update: in case it’s not obvious, I am sorry. I could not help it.
Em dashes are really good actually and a standard stylistic choice for non-technical writing, particularly outside the US.
My writing used to be littered with them, but I now eschew the em in favour of en, as it has become too strong an anti-shibboleth.
I have also taken to being sloppier in my prose, as I’ve had stories rejected for being “written by AI” - when they’re shorts I wrote more than a decade ago. Reworked them to sound like a moron, accepted. Sigh.
I have a similar issue. I tend to have a very “structured” type of writing. Say on slack or Reddit for example. Using markdown formatting. Lists with bulletpoints etc. And I tend to write long detailed explanations, sometimes too long if I am being honest.
But now I find myself adding noise and imperfections to my writing (not that it was perfect) to make it more human, which is kinda silly.
I think it's an interesting perspective, because translation is one of the jobs that I (a) hear is the first to lose work due to AI, and (b) often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
> often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
As one of such people, I think there is a nuance to it. AI is great when you’re translating something to yourself. But when translating things for others, more caution and human judgement is needed. Espesially when translating instruction manuals, where bad wording could cause someone to injure themself.
This. I put things through Google translate all the time and they're always unreliable. Sometimes they're correct, sometimes I need to know roughly what the original said. Infamously, Google used to say "geiler Typ" meant "horny guy" when it means "awesome guy". Google used to think "geil" meant "horny" in general, which it can but not usually
Google translate is primitive compared to Claude at translations.
Google Translate is at the bottom of the barrel. All other AI translation tools are vastly superior. You'd want to evaluate those, and forget about Google Translate completely.
Exactly, it's never about absolute results, it's always
Expected Value (Upside, given time/cost savings + Downside, given %reliability).
So, every task falls under a spectrum
Language is incredibly complex. I remember a TikTok from a bilingual English-Korean speaker comparing the English subtitles from a Squid Game scene to what was actually being said by the characters. The nuance and info density lost in translation made the subtitles feel completely remedial. Americans were basically watching a different show altogether.
There are translators and there are translators. Translating legal/business documents is a completely different thing from translating movies/books/games.
I can confidently say that LLMs do a better job than the average traditionally published fictions in my country, at least when the original works are in English. Every single time I watch a subbed movie there will be some lines noticeably wrong.
Translators already started losing jobs due to machine translation a decade ago (e.g. DeepL), before LLMs. Remuneration going down made it more difficult to make a living as a translator already then, even if you still received offers.
Not all translations are the same. Literary translations are often works of art in and of themselves, and automating them would be missing the point entirely, like automating homework or weightlifting at the gym. I don't really know what's the state of the art, but I do buy that, on the other hand, translating toaster manuals or generic copy could soon be automatic.
Yup. If you are bilingual, you quickly realize how some translations are very bad. How some translations are very good. And how hard it is to translate. With dry, simple text, it might be easy. But when it involves art? Some jokes don't translate directly. There is pun. Sounds of words. Double meaning. Ambiguity. Cultural background. The creation of new words.
It can be reasonably argued that some poetry can be impossible to translate from some languages to others. A poem might be explained, but by a lenghty, dissecting explanation, that completely loses the point of it.
Or if you compare a poetic translation to a literal one, of different translations of the same work to the same language to each other.
When it's one one-hundredth the cost, "good enough" is generally good enough.
"Could not connect to translation service" was apparently good enough for someone, so the bar must be extremely low.
https://www.reddit.com/r/funny/comments/3e786n/chinese_hair_...
On the other hand, a lot of people become extremely put off by the smallest sign of ai slop. And the llms have a tendency to impart their style to any text they touch.
It'll be a similar theme for all facets of work involving any language, slowly - human or code. We'll parrot about humans in the loop this and that, but I think it'll be less humans in the loop over time and I think most people will even be willing to settle for a slightly more mediocre translation or coded project. It all comes back to our dopamine addiction, where we like fast feedback. And the oligarchs like tools to suppress wages. We will be our own demise for not advocating for either UBI or job protections, instead, happily using the technology while also rolling our eyes that it could never replace us.
So i assume this post is just a bit of writing out frustration, but i'm always hoping that "AI can't do it" posts to include examples.
A list of "Examples AI will silently fail at" would be a lot more interesting, and might just convince your next potential client to _not_ use AI.
What’s unfortunate is that the market that is willing to pay for high-quality human translation has shrunken considerably.
Is it that unfortunate? Tasks that don't require high-quality translation now don't need human labor. We should be celebrating.
The sad part is that we haven't figured out how to distribute our resources fairly to these people even thought their services aren't required as often. Instead we just take their wages and give them to the top 0.1%
It’s unfortunate because we are seeing more poor translations in all domains, and users suffer from it. It’s part of a general enshittification of things. There are few contexts where low-quality translations don’t constitute a degradation of user experience.
Just one funny example I saw recently: On the Amazon website, a submit button labeled “Go” in English was translated to something that translated back would be “Walking”. That’s the kind of thing that would be exceedingly unlikely to happen with a human translator.
Humans are really bad at noticing trajectories. They see the current situation. They know what the situation was 5 years ago. But for some reason they do not believe that there is a trajectory. They view the present state as the final destination.
It’s been basically the same for 3 years now. Are you sure we’re the ones who can’t see trends?
Sure, just like AI enthusiasts seem to be unfamiliar with the concept of local maxima...
I worked at large Japanese bank in New York and happened to sit near Chief US Economist next to his Japanese translator. She would occasionally ask about certain idioms. I remember explaining what a wildcat strike was for instance. But it must have been pretty tough because the guy was prolific in his commentary.
Presumably the people paying the author for translation services are aware of AI, but for whatever reason are choosing a humans services instead. IMO it would be a form fraud to heavily rely on AI and not disclose that to the customer.
It's quite ironic as the transformer architecture that powers most generative AI was invented for language translation :)
> “Great. So, do you use AI a lot at work?”
> “Oh, I can’t! It’s really not reliable enough.”
Gell-Mann Amnesia strikes again.
All I got out of this article is that he should have went home and dumped it into chatgpt just to see what happened; then if it did as good a job as him, he should start looking for other places he can add value that AI can't.
she did. Did you remember to read the article?
The article does not say that. The author doesn't take the text the other person dumped into ChatGPT and evaluate its quality. That is what OP is referring to.
The article clearly implies she has tried so previously.
From the phrasing of the sentence, with the incorrect gender and the generic nature of the comment, obviously not.
This is just about the worst career you could be in right now. Of course people are just going to upload it to ChatGPT. Processing text is its forte.
wrt. the end of the story, it will be interesting to see if people start noticing their Dunning-Kruger bias as a result of LLMs.
Specifically: LLMs make it really easy to misunderestimate the complexity of fields other than your own. (You can see this with a lot of vibecoded projects, for example – once they hit the wall of complexity, they stall out or start finding ugly patches for fundamental design issues, etc.)
I don't think this sort of cultural change will happen short-term, though.
> LLMs make it really easy to misunderestimate the complexity
In my experience this is a real problem. Just yesterday I asked my LLM to create a piece of software that could help me build an 'ambilight-like experience' through my home assistant. It did something that seems to work as I expected, but there is a lot of theory that I just brushed past. It would be pretty easy for me to assume that I would be able to replicate this feature from scratch 'now that I understand the problem'.
Agreed. LLMs are really terrific at sounding like they know exactly what they are talking about. Fable is the best yet. Beautiful, thorough explanations with absolute certainty, which under even light scrutiny turn out to be mostly bullshit.
I still love the tool, but remain as convinced as ever that AGI does not lie at the end of this particular path.
Translating is one thing that artificial intelligence undeniably excels at, and the value of this alone is enough to underpin the trillion dollar valuations of the gigantic AI companies.
Translation is a gigantic boon for business, but just as important for human connection, for culture, science, art, and entertainment. The value of automatic and cheap translation between all languages, this tower of Babylon, is immeasurable.
Human translators will always be better than any AI at their job. But they don't have unlimited time and energy, and they aren't cheap. AI makes good to great translations available to everybody.
True, and relevant (I live with a professional editor)... yet I immediately think of Ximm's Law:
Every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Lemma: contemporary implementations have already improved; they're just unevenly distributed.
No one assumes that AI systems won't be improved upon. What people don't assume is that progress will be infinite in every domain cheaply forever.
From the post:
> Ah, you can’t fire me, I’m self-employed!
I don't understand thinking like this. I think companies can certainly fire their contractors.
I agree with the take, but it's a temporary one, the sad reality is that we will be literally inferior soon, there will be a point where we will not trust human input without counter check by AI, we need to remember that we are kinda at the beginning of the AI era, in 5 to 10 years it's very unlikely that a human translator or software engineers will do better than the tooling we will have.
There is already a tipping point now in software engineering where we prefer to ask AI instead of humans because we believe accuracy will be better, see SO death as an example or just see the current state of online dev communities, it's getting deserted and between team members at work, we can also notice that people speak less and less.
Sad but I believe it.
> we will be literally inferior soon
This plague of misanthropic doom is itself pretty depressing. Why do so many people think LLMs are in any way on a path to compete with human brains? Why do you think so little of yourself? The brain is magnificent and complex in ways that we are unable to decipher anytime soon, and it does way more than an LLM. Way, way more.
I don't talk specifically about LLMs but AI in general, it's an important distinction because tooling is currently what make models useful and more performant.
When I say we, I mean the general population really. There0-'ll always be the super bright ones, sure, but we gotta be realistic here. Most people already struggle to make any meaningful contribution because it's so hard to compete, and that gap is just gonna get bigger and bigger.
I agree the brain is pretty magnificent, but when it comes to stuff like language, figuring out if an idea actually works, or running business stuff, it's pretty obvious we'll be inferior. AI can already innovate and come up with new things way faster than any human could, so at some point (soon) => the majority of contributions are just gonna come from AI, not from us.
The thing is that AI is not some inevitable force of nature that must just be contended with and weathered. It is an active choice by our society to develop it and it is a choice by our society how we should use it, if at all.
We would all do well to remember that and remember that each and every advancement and use case regarding AI is the result of choices by people (or the groups of people we call corporations) and are oftentimes motivated by the profit motive, not the best interest of humanity.
We could make different choices up to and including our own Butlerian Jihad where we ban all forms of AI but we could also do everything we can to prevent the worst fallout short of that.
There are only two types of problems in the universe: 1) those posed by the laws of physics 2) those posed by human choices
The problem of AI is one of the latter.
This is anecdata, but in my experience with myself and my coworkers, it is not that we believe the AI will be more accurate in software engineering, but that the answer will come faster and be more tailored to our exact problems. If I have to search SO, I have to find the answer and then tweak it to fit my codebase, but with AI tooling, the AI is already basing its answer around my code.
I think we actually do believe it, do you believe Fable 5+GPT-5.5(+ the whole model zoo) in loop with adversarial (no budget limit) or a 10-year experienced SWE?
We are talking about "codebases" but realistically we won't even be checking the filetree of them soon, it will be all blind, containerized and verified with pseudo guarantees which are good enough to build serious things. We don't even write documentation for humans anymore, we need to look at the trends and the reality within companies, most developers became "callcenter agents" in a matter of only 2 years and literally most of them are not even using proper automated tooling yet as we can see the "vibe coding" trend with Claude Code which is weak, by far most work done daily by developers is already automatable entirely, but with exceptions, sure, but in a few years those exceptions will become rare.
There will be niche problems about legacy products, sure, but legacy products will all be replaced over time, if we think in depth, why do we even need that many languages, that many tools? Tomorrow AI will write 99% if not all code existing ("code" doesn't even matter anyway), so it's much better if it's specific to AI and not playing this dance where we think we are doing a meaningful human contribution on an "AI-made codebase".
For context, I have 2 decades of software dev behind me.
> there will be a point where we will not trust human input without counter check by AI
That's nonsense. There is zero reason to believe that AI (with the current techniques) will ever become reliable enough to let it do its own thing, let alone better than a human. It's been years of development and you still can't trust it to get basic facts correct, not even "well it's better than it used to be". Saying it'll replace humans in 5-10 years is a fantasy (or a prediction that people are stupid enough to fall for hype, I guess).
You come from the principle that humans are reliable at first which is partly right but also wrong in so many scenarios, you can even see lately the CVE spree happening, which demonstrates that human-made codebases have serious vulnerabilities and without the help of AI, we probably won't even know about them which proves that humans are not that "reliable", the current societal structure is also built around the fact that humans can't really be trusted, nothing really different with AI, we can't fully trust them like we can't fully trust humans.
It's not a fantasy, I would bet that no serious engineer nowadays is putting in prod a codebase not AI reviewed meaning we already can't work on our own, we must factor in the on-going decline of human capabilities (at least developers) as well of course.
I'm not really saying this because of any sort of hype, but I can personally relate where I went from actually coding to NEVER CODE in less than 2 years, and everyone around me is the same thing, what it will be in 5 years?
Knowing that really, most developers aren't even using proper tooling yet so they are very slow compared to what they could be, I mean how many people we hear saying they can't even saturate an Anthropic Max 20 subscription? I saturated 7 accounts the last 2h alone, it's because they haven't entirely rethought their workflows yet.
It can spot mistakes made by a human if asked to review code or write tests.
GP is is over the top ins saying humans will "be inferior soon" but AI can be a nice additional check so AI review might be come standard.